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Vol-3840
urn:nbn:de:0074-3840-6




HEXED-L3MNGET 2024
HEXED 2024 and L3MNGET 2024 Workshops Joint Proceedings


Joint Proceedings of the Human-Centric eXplainable AI in Education and the Leveraging Large Language Models for Next Generation Educational Technologies Workshops (HEXED-L3MNGET 2024)
co-located with 17th International Conference on Educational Data Mining (EDM 2024)

Atlanta, Georgia, USA, July 14, 2024.


Edited by

Juan D. Pinto, University of Illinois Urbana-Champaign, USA
Eamon Worden, Worcester Polytechnic Institute, USA
Anthony Botelho, University of Florida, USA
Lea Cohausz, University of Mannheim, Germany
Clayton Cohn, Vanderbilt University, USA
Mingyu Feng, WestEd, USA
Neil Heffernan, Worcester Polytechnic Institute, USA
Arto Hellas, Aalto University, Finland
Lan Jiang, University of Illinois at Urbana-Champaign, USA
David Joyner, Georgia Tech, USA
Tanja Käser, EPFL, Switzerland
Juho Kim, KAIST, Korea
Andrew Lan, University of Massachusetts Amherst, USA
Chenglu Li, University of Utah, USA
Joshua Littenberg-Tobias, WGBH Education, USA
Qianhui Liu, University of Illinois Urbana-Champaign, USA
Christopher MacLellan, Georgia Tech, USA
Steven Moore, Carnegie Mellon University, USA
Maciej Pankiewicz, University of Pennsylvania, USA
Luc Paquette, University of Illinois Urbana-Champaign, USA
Zach A. Pardos, University of California Berkeley, USA
Anna Rafferty, Carleton College, USA
Adish Singla, Max Planck Institute for Software Systems, Germany
Shashank Sonkar, Rice University, USA
Vinitra Swamy, EPFL, Switzerland
Rose E. Wang, Stanford University, USA
Candace Walkington, Southern Methodist University, USA





Table of Contents

Human-Centric eXplainable AI in Education (HEXED 2024)

Leveraging Large Language Models for Next Generation Educational Technologies (L3MNGET 2024)


2024-11-01: submitted by Juan D. Pinto, metadata incl. bibliographic data published under Creative Commons CC0
2024-11-22: published on CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073) |valid HTML5|